import gradio as gr

import pandas as pd

from sklearn.model_selection import train_test_split

housing = pd.read_csv("housing.csv")
train_set, test_set = train_test_split(housing, test_size=0.2, random_state=10)

## 2. clean the missing values
train_set_clean = train_set.dropna(subset=["total_bedrooms"])
train_set_clean

## 2. derive training features and training labels
train_labels = train_set_clean["median_house_value"].copy() # get labels for output label Y
train_features = train_set_clean.drop("median_house_value", axis=1) # drop labels to get features X for training set


## 4. scale the numeric features in training set
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler() ## define the transformer
scaler.fit(train_features) ## call .fit() method to calculate the min and max value for each column in dataset

train_features_normalized = scaler.transform(train_features)
train_features_normalized

from sklearn.linear_model import LinearRegression ## import the LinearRegression Function
lin_reg = LinearRegression() ## Initialize the class
lin_reg.fit(train_features_normalized, train_labels) # feed the training data X, and label Y for supervised learning

import numpy as np
def predict_price(input1, input2, input3, input4, input5, input6, input7, input8):
  features = np.array([[float(input1), float(input2), float(input3), float(input4), float(input5), float(input6), float(input7), float(input8)]])
  print("recived features are: ", features)
  price = lin_reg.predict(features)
  return price

input_module1 = gr.inputs.Textbox(label = "Input Feature 1")
input_module2 = gr.inputs.Textbox(label = "Input Feature 2")
input_module3 = gr.inputs.Textbox(label = "Input Feature 3")
input_module4 = gr.inputs.Textbox(label = "Input Feature 4")
input_module5 = gr.inputs.Textbox(label = "Input Feature 5")
input_module6 = gr.inputs.Textbox(label = "Input Feature 6")
input_module7 = gr.inputs.Textbox(label = "Input Feature 7")
input_module8 = gr.inputs.Textbox(label = "Input Feature 8")

output_module1 = gr.outputs.Textbox(label = "Output Text")

gr.Interface(fn=predict_price,
             inputs=[input_module1, input_module2, input_module3,
                     input_module4, input_module5, input_module6,
                     input_module7, input_module8],
             outputs=[output_module1]
            ).launch()